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dc.contributor.authorMakantasis, Konstantinos-
dc.contributor.authorDoulamis, Anastasios D.-
dc.contributor.authorDoulamis, Nikolaos D.-
dc.contributor.authorNikitakis, Antonis-
dc.date.accessioned2024-08-20T08:50:08Z-
dc.date.available2024-08-20T08:50:08Z-
dc.date.issued2018-
dc.identifier.citationMakantasis, K., Doulamis, A. D., Doulamis, N. D., & Nikitakis, A. (2018). Tensor-based classification models for hyperspectral data analysis. IEEE Transactions on Geoscience and Remote Sensing, 56(12), 6884-6898.en_GB
dc.identifier.urihttps://www.um.edu.mt/library/oar/handle/123456789/125523-
dc.description.abstractIn this paper, we present tensor-based linear and nonlinear models for hyperspectral data classification and analysis. By exploiting the principles of tensor algebra, we introduce new classification architectures, the weight parameters of which satisfy the rank-1 canonical decomposition property. Then, we propose learning algorithms to train both linear and nonlinear classifiers. The advantages of the proposed classification approach are that: 1) it significantly reduces the number of weight parameters required to train the model (and thus the respective number of training samples); 2) it provides a physical interpretation of model coefficients on the classification output; and 3) it retains the spatial and spectral coherency of the input samples. The linear tensor-based model exploits the principles of logistic regression, assuming the rank-1 canonical decomposition property among its weights. For the nonlinear classifier, we propose a modification of a feedforward neural network (FNN), called rank-1 FNN, since its weights satisfy again the rank-1 canonical decomposition property. An appropriate learning algorithm is also proposed to train the network. Experimental results and comparisons with state-of-the-art classification methods, either linear (e.g., linear support vector machine) or nonlinear (e.g., deep learning), indicate the outperformance of the proposed scheme, especially in the cases where a small number of training samples is available.en_GB
dc.language.isoenen_GB
dc.publisherInstitute of Electrical and Electronics Engineersen_GB
dc.rightsinfo:eu-repo/semantics/restrictedAccessen_GB
dc.subjectHyperspectral imagingen_GB
dc.subjectRemote-sensing imagesen_GB
dc.subjectTensor productsen_GB
dc.subjectTensor algebraen_GB
dc.subjectNeural networks (Computer science)en_GB
dc.titleTensor-based classification models for hyperspectral data analysisen_GB
dc.typearticleen_GB
dc.rights.holderThe copyright of this work belongs to the author(s)/publisher. The rights of this work are as defined by the appropriate Copyright Legislation or as modified by any successive legislation. Users may access this work and can make use of the information contained in accordance with the Copyright Legislation provided that the author must be properly acknowledged. Further distribution or reproduction in any format is prohibited without the prior permission of the copyright holder.en_GB
dc.description.reviewedpeer-revieweden_GB
dc.identifier.doi10.1109/TGRS.2018.2845450-
dc.publication.titleIEEE Transactions on Geoscience and Remote Sensingen_GB
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